Assessment of trace metals using chemometric analysis in Kuantan River, East Coast Malaysia

Chemometric techniques were applied to identify and assess the trace metals of Kuantan River water. Water samples from nine sampling stations were taken from downstream of the estuary towards the upstream of Kuantan River. The following trace metals were selected for analysis i.e. Hg, Cd, Pb, As, Co...

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Bibliographic Details
Main Authors: Faudzi, Fikriah, Yunus, Kamaruzzaman, Miskon, Mohd Fuad, Azid, Azman
Format: Article
Language:English
Published: Zibline International Publisher 2019
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Online Access:http://irep.iium.edu.my/74567/1/74567_Assessment%20of%20trace%20metals%20using%20chemometric%20analysis%20in%20Kuantan%20River%2C%20East%20Coast%20Malaysia.pdf
http://irep.iium.edu.my/74567/
https://jcleanwas.com/archives/2jcleanwas2019/2jcleanwas2019-01-04.pdf
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Summary:Chemometric techniques were applied to identify and assess the trace metals of Kuantan River water. Water samples from nine sampling stations were taken from downstream of the estuary towards the upstream of Kuantan River. The following trace metals were selected for analysis i.e. Hg, Cd, Pb, As, Co and Cr. To evaluate chemical variation on the variables, multivariate statistical methods like principal components analysis (PCA), cluster analysis (CA) and discriminant analysis (DA) were performed. The homogeneity characteristics between the stations were integrated by cluster analysis, which were represented by dendrograms and were classified according to their surrounded activities. PCA was identified four factors namely Cr, Hg, Pb and Co explaining 77.8% of the total variance. PCA revealed that Kuantan River is strongly affected by point sources such as commercial wastewater, industries and non-point sources especially from agriculture activities. The CA classified three clusters as commercial centre (CC), residential areas (RA) and agricultural areas (AA). AA located in the upstream is less polluted than RA, while CC located in the downstream is more polluted than RA. These statistical approaches and results yielded useful information including identification of pollution sources/factors and understanding temporal variations in river water for effective river water quality management.